An Efficient Sampling-Based Path Planning for the Lunar Rover with Autonomous Target Seeking

Abstract

This paper presents an efficient path planning method for the lunar rover to improve the autonomy and exploration ability in the complex and unstructured lunar surface environment. Firstly, the safe zone for the rover’s motion is defined, based on which a detecting point selection strategy is proposed to choose target positions that meet the rover’s constraints. Secondly, an improved sampling-based path planning method is proposed to get a safe path for the rover efficiently. Thirdly, a map extension strategy for the unstructured and continually varying environment is included to update the roadmap, which takes advantage of the historical planning information. Finally, the proposed method is tested in a complex lunar surface environment. Numerical results show that the appropriate detecting positions can be selected autonomously, while a safe path to the selected detecting position can be obtained with high efficiency and quality compared with the Probabilistic Roadmap (PRM) and A* search algorithm.